climpred.prediction.compute_hindcast(hind, verif, metric='pearson_r', comparison='e2o', dim='init', max_dof=False, add_attrs=True, **metric_kwargs)[source]

Verify hindcast predictions against verification data.

  • hind (xarray object) – Hindcast ensemble. Expected to follow package conventions: * init : dim of initialization dates * lead : dim of lead time from those initializations Additional dims can be member, lat, lon, depth, …
  • verif (xarray object) – Verification data with some temporal overlap with the hindcast.
  • metric (str) – Metric used in comparing the decadal prediction ensemble with the verification data. (see get_metric_class() and Metrics).
  • comparison (str) –

    How to compare the decadal prediction ensemble to the verification data:

    • e2o : ensemble mean to verification data (Default)
    • m2o : each member to the verification data

    (see Comparisons)

  • dim (str or list) – dimension to apply metric over. default: ‘init’
  • max_dof (bool) –

    If True, maximize the degrees of freedom by slicing hind and verif to a common time frame at each lead.

    If False (default), then slice to a common time frame prior to computing metric. This philosophy follows the thought that each lead should be based on the same set of initializations.

  • add_attrs (bool) – write climpred compute args to attrs. default: True
  • metric_kwargs (**) – additional keywords to be passed to metric (see the arguments required for a given metric in Metrics).

Predictability with main dimension lag without dimension dim

Return type:

skill (xarray object)